Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

This paper proposes a new model for extracting an interpretable sentence embedding by
introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the …

In this paper, we present the gated self-matching networks for reading comprehension style
question answering, which aims to answer questions from a given passage. We first match …

Chest X-rays are one of the most common radiological examinations in daily clinical
routines. Reporting thorax diseases using chest X-rays is often an entry-level task for …

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and
domain-specific knowledge in order to learn effectively from the small, supervised training …

In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of
the skip-gram model by explicitly distinguishing left and right context in word prediction. In …

Context representations are central to various NLP tasks, such as word sense
disambiguation, named entity recognition, coreference resolution, and many more. In this …

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks,
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

Real-time emotion recognition (RTER) in conversations is significant for developing
emotionally intelligent chatting machines. Without the future context in RTER, it becomes …